System and method for evaluating artificial intelligence agents deployed in an enterprise computing environment

Abstract

A computer-implemented system and method for evaluating an AI agent associated with an enterprise computing environment by aggregating a plurality of AI agents associated with the enterprise computing environment, and evaluating, with an agent evaluation unit, a selected AI agent of the plurality of AI agents using evaluation data, wherein the evaluation data includes operational behavior data and trustworthiness data. The agent evaluation unit can be configured to determine a total agent evaluation score for the selected AI agent from a plurality of category specific evaluation scores. The agent specific categories can have associated therewith a category evaluation score and the agent specific categories can include categories associated with the operational behavior and the trustworthiness of the selected AI agent. An AI-based intervention can be applied in response to the total agent evaluation score.

We claim: 
     
         1 . A computer-implemented artificial intelligence (AI) agent evaluation system for evaluating an AI agent associated with an enterprise computing environment, comprising
 an agent aggregation unit for aggregating together a plurality of AI agents associated with the enterprise computing environment, and   an agent evaluation unit for evaluating a selected AI agent of the plurality of AI agents using evaluation data, wherein the evaluation data includes operational behavior data and trustworthiness data, and wherein the agent evaluation unit includes
 an agent scoring unit for determining a total agent evaluation score for the selected AI agent, wherein the total agent evaluation score is determined from a plurality of category evaluation scores from a plurality of agent specific categories, wherein each of the plurality of agent specific categories has associated therewith a category evaluation score, wherein the plurality of agent specific categories includes categories associated with the operational behavior and the trustworthiness of the selected AI agent, and 
 an intervention unit configured for automatically initiating an AI-based intervention in the enterprise computing environment in response to the total agent evaluation score of the selected AI agent received from the agent scoring unit, 
   wherein the agent evaluation unit is configured to enhance computational efficiency and decision accuracy within the enterprise computing environment by dynamically adapting agent tasking and deployment based on the total agent evaluation score.   
     
     
         2 . The computer-implemented system of  claim 1 , wherein the agent evaluation unit further comprises
 an agent selection unit configured to select, for assignment to a task, one or more of the AI agents of the plurality of AI agents having the total agent evaluation score that satisfies one or more predefined selection criteria,   wherein the agent evaluation unit enables dynamic and automated control of agent deployment based on the total agent evaluation score, thereby improving operational efficiency of the enterprise computing environment and decision accuracy of the selected AI agent.   
     
     
         3 . The computer-implemented system of  claim 2 , wherein the plurality of agent specific categories includes a planning accuracy category, a tool precision category, a knowledge reliability category, a safety and compliance category, and a human collaboration category. 
     
     
         4 . The computer-implemented system of  claim 2 , wherein the trustworthiness categories of the plurality of agent specific categories includes a safety and compliance category and a knowledge reliability category. 
     
     
         5 . The computer-implemented system of  claim 4 , wherein the operational behavior categories of the plurality of agent specific categories includes a planning accuracy category, a tool precision category, and a human collaboration category. 
     
     
         6 . The computer-implemented system of  claim 5 , wherein the agent scoring unit is configured to employ a scoring logic framework that assigns a selected weight to each of the agent specific categories. 
     
     
         7 . The computer-implemented system of  claim 6 , wherein the agent scoring unit is configured to automatically and dynamically adjust the weights assigned to each agent specific category based on one or more weighting factors and an operational context of the AI agent. 
     
     
         8 . The computer-implemented system of  claim 7 , wherein the agent scoring unit employs a weighted aggregation technique to determine the total agent evaluation score based on the category evaluation scores of the plurality of agent specific categories. 
     
     
         9 . The computer-implemented system of  claim 7 , wherein the agent scoring unit employs an unweighted technique that determines an arithmetic mean of the category evaluation scores. 
     
     
         10 . The computer-implemented system of  claim 7 , wherein the agent scoring unit applies the scoring logic framework such that:
 the category evaluation score associated with the planning category is determined by analyzing a correct number of steps executed by the AI agent and then dividing the correct number of steps by a total number of steps;   the category evaluation score associated with the tool precision category is determined by comparing a number of correct tool invocations performed by the AI agent to a total number of tool invocations attempted by the AI agent;   the category evaluation score associated with the knowledge reliability category is determined based on one or more measurable indicators including a staleness check and a contradiction rate;   the category evaluation score associated with the safety and compliance category is determined by determining a proportion of interactions that occur without triggering a safety incident; and   the category evaluation score associated with the human collaboration category is determined based on one or more of an escalation accuracy, a feedback reinforcement effectiveness, an oversight compliance, and a transparency quality.   
     
     
         11 . The computer-implemented system of  claim 7 , wherein the intervention unit automatically performs an AI-based intervention when the total evaluation score is below a selected threshold, and wherein the AI-based intervention includes an agent related corrective action. 
     
     
         12 . A computer-implemented method for evaluating an AI agent associated with an enterprise computing environment, the method comprising
 aggregating, with an agent aggregation unit, a plurality of AI agents associated with the enterprise computing environment, and   evaluating, with an agent evaluation unit, a selected AI agent of the plurality of AI agents using evaluation data, wherein the evaluation data includes operational behavior data and trustworthiness data, and wherein the agent evaluation unit is configured to:
 determine, with an agent scoring unit, a total agent evaluation score for the selected AI agent, wherein the total agent evaluation score is determined from a plurality of category evaluation scores from a plurality of agent specific categories, wherein each of the plurality of agent specific categories has associated therewith a category evaluation score, wherein the plurality of agent specific categories includes categories associated with the operational behavior and the trustworthiness of the selected AI agent, and 
 automatically initiating, with an intervention unit, an AI-based intervention in the enterprise computing environment in response to the total agent evaluation score of the selected AI agent received from the agent scoring unit, 
   wherein the agent evaluation unit is configured to enhance computational efficiency and decision accuracy within the enterprise computing environment by dynamically adapting agent tasking and deployment based on the total agent evaluation score.   
     
     
         13 . The computer-implemented method of  claim 12 , further comprising selecting, with an agent selection unit of the agent evaluation unit, for assignment to a task, one or more of the AI agents of the plurality of AI agents having the total agent evaluation score that satisfies one or more predefined selection criteria,
 wherein the agent evaluation unit enables dynamic and automated control of agent deployment based on the total agent evaluation score, thereby improving operational efficiency of the enterprise computing environment and decision accuracy of the selected AI agent.   
     
     
         14 . The computer-implemented method of  claim 13 , wherein the plurality of agent specific categories includes a planning accuracy category, a tool precision category, a knowledge reliability category, a safety and compliance category, and a human collaboration category. 
     
     
         15 . The computer-implemented method of  claim 13 , wherein the trustworthiness categories of the plurality of agent specific categories includes a safety and compliance category and a knowledge reliability category, and the operational behavior categories of the plurality of agent specific categories includes a planning accuracy category, a tool precision category, and a human collaboration category. 
     
     
         16 . The computer-implemented method of  claim 15 , further comprising employing a scoring logic framework for assigning a selected weight to each of the agent specific categories. 
     
     
         17 . The computer-implemented method of  claim 16 , further comprising automatically and dynamically adjusting the weights assigned to each agent specific category based on one or more weighting factors and an operational context of the AI agent. 
     
     
         18 . The computer-implemented method of  claim 17 , further comprising applying a weighted aggregation technique to determine the total agent evaluation score based on the category evaluation scores of the plurality of agent specific categories. 
     
     
         19 . The computer-implemented method of  claim 18 , further comprising applying the scoring logic framework such that:
 the category evaluation score associated with the planning category is determined by analyzing a correct number of steps executed by the AI agent and then dividing the correct number of steps by a total number of steps;   the category evaluation score associated with the tool precision category is determined by comparing a number of correct tool invocations performed by the AI agent to a total number of tool invocations attempted by the AI agent;   the category evaluation score associated with the knowledge reliability category is determined based on one or more measurable indicators including a staleness check and a contradiction rate;   the category evaluation score associated with the safety and compliance category is determined by determining a proportion of interactions that occur without triggering a safety incident; and   the category evaluation score associated with the human collaboration category is determined based on one or more of an escalation accuracy, a feedback reinforcement effectiveness, an oversight compliance, and a transparency quality.   
     
     
         20 . The computer-implemented method of  claim 7 , further comprising automatically performing, with the intervention unit, an AI-based intervention when the total evaluation score is below a selected threshold, and wherein the AI-based intervention includes an agent related corrective action.